Challenge: Existing studies have not exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning.
Approach: They propose an Implicit Cause-Effect interaction framework which captures the implicit intra- and inter-event interactions by incorporating the privileged information for reasoning.
Outcome: The proposed framework captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning.

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Challenge: Event Causality Identification (ECI) ignores crucial event structure and cause-effect component information, making it struggle for downstream applications.
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Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)

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Challenge: Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text.
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Challenge: Existing methods for ECI rely on causal features and external knowledge, but these methods fail in two dimensions: causal features between events in texts often lack explicit clues and external information may introduce bias.
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Emotion–Cause Pair Extraction in Conversations via Semantic Decoupling and Alignment (2026.findings-acl)

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Challenge: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering causes within a dialogue.
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Challenge: Existing methods for event causality identification (ECI) do not consider event causal label information and interaction information between event pairs.
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Challenge: Existing methods for Event Causality Identification (ECI) rely on external toolkits or human annotation to obtain training signals.
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Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)

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Challenge: Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger.
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Enhancing Event Causality Identification with LLM Knowledge and Concept-Level Event Relations (2025.coling-main)

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Challenge: Existing methods to identify causal relationships between events often overlook the dependencies between similar events.
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Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning (2024.findings-emnlp)

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Challenge: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterrances in unannotated conversations.
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